Since Amazon released Lambda in late 2014, the notion of serverless applications and function-as-a-service has steadily gained steam. Being able to focus on application code and simplifying infrastructure management is alluring, but traditional monitoring methods are no longer applicable. With less visibility, it becomes even more important to take advantage of the available monitoring methods. In this post, we discuss those monitoring methods, CloudWatch Metrics and CloudWatch Logs.

I set out to find a credit mechanism or hard-coded limit in packets per second in AWS EC2. After all my findings set out in this series so far, I had one more test to perform around t2.unlimited. I wanted to see how “unlimited” it is and the difference it makes in packet throughput on capable instance types. This post is about my findings.

I don’t know what to say about this post… I found something weird while investigating PPS on EC2. It seems to correlate with CPU credits on t1/t2/t3 instances, but is consistently inconsistent in presentation. It only shows up when you track the stats yourself, because Cloudwatch doesn’t show the 1-second granularity needed to see these numbers.

While benchmarking packets per second (PPS) in AWS EC2 and searching for hard-coded or other software-based limitations, my early findings suggested that there definitely was a credit mechanism, complete with network throttling, in place. I now know that to be false, since finding the guaranteed throughput / best effort mechanic.

Amazon Elastic Beanstalk allows you to quickly provision the infrastructure needed for an entire application without the hassle of managing the configuration of EC2 instances, Elastic Load Balancers, Auto Scaling, and many other AWS services. Elastic Beanstalk also automatically monitors these resources and provides a simplified view into your application’s health.